In this digital age, merchants are challenged by customers who want near instantaneous transactions across multiple channels and the need to reduce fraudulent transactions. At the same time, merchants struggle to enhance fraud detection for the rapidly growing market of digital goods.
With the prevalence of computers, mobile telephones and the Internet, buyers and sellers can now interact without seeing one another; card-not-present (CNP) transactions in which the merchant never sees the payment card are now much more common. As e-commerce becomes more popular and digital sales and delivery more common, merchants struggle to contain fraudulent payments without inconveniencing customers. A glitch during an e-commerce transaction can result in loss of the transaction for the merchant.
Unfortunately, card-not-present transactions—many of which are performed using computers, a telephone or mobile devices—are responsible for a great deal of fraud. Unfortunately, increasing fraud controls and tightening up fraud detection algorithms in order to detect fraud during a transaction can result in a good transaction being denied and a good customer being turned away.
One particular area ripe for fraud is the sale of digital goods: mobile telephone top-up transactions, electronic books, gift cards, electronic tickets, etc. The sale of digital goods does not require the merchant to see a physical payment card, and moreover, consumers expect to receive their digital goods immediately, leaving the merchant less time to detect potential fraud. Some fraudsters take advantage of weaknesses in e-commerce systems to steal additional goods, while others break into a retailers system in order to steal customer payment card information. In addition, a transaction for digital goods requires no physical address as the digital goods are often delivered via e-mail, text message or credit to a mobile account.
Most fraud detection systems are not yet able to handle the increase in online card-not-present transactions and especially the sale and delivery of digital goods. Existing systems may require time to verify the cardholder's identity and payment, which does not work well with digital goods when immediate delivery is expected. Or, existing systems cannot accurately verify payment details and detect fraud due to the speed of order fulfillment that is required.
Any fraud detection system is measured by its ability to detect fraudulent transactions and to accept legitimate transactions whenever possible. It can be a challenge to optimize for both situations. Existing fraud detection systems use fraud detection models that must be retrained by a human modeler or by a predetermined process using newly available fraud data in order to enhance detection. For example, once a fraud model has been trained and is in production, it has established criteria for fraud detection. The fraud model relies upon the human to retrain the model and to reestablish the criteria—a manual, time-consuming and potentially inaccurate process. Even if a model is retrained via a process using available fraud data that has been reported, there is typically a lag time of anywhere from one to three months from when the fraud actually occurred. Thus, it is often too late to catch similar fraud, and fraudulent transactions stemming from the original fraud have long been processed. Further, traditional fraud models use historical data from a customer or payment device in order to assess risk for that particular customer or payment device, but, some fraud cannot be detected this way. Many transactions involve a new customer, a new payment device or a new stored value account (SVA); traditional models do not do well assessing risk in these situations.
Accordingly, new methods and systems are needed that allow a fraud detection system to change the behavior of fraud detection models in real time.